And this path through the mediator, From x to the mediator,

The mediator is from Pune, India.

A good mediator can make a deal go smoothly.

Because the mediator will explain all the variance in Y that X was accounting for.

Uh, but for now, we'll just keep them as predictor and outcome. I'll use M for the mediator.

I am saying we can predict Y from the mediator.

And this 0.22 was 0.28 before adding in the mediator.

How nice. As a mediator, or to stop the bleeding?

Right? Uh, then you wanna make sure that the mediator and

So, as we go from x to the mediator to y, If those events happen over time so that the mediator follows x and the outcome follows the mediator, then again, we're moving towards scenarios where we can make stronger claims or stronger arguments of that causality.

One, just think about what the word mediation or a mediator uh, means.

But X might remain significant even in the face of a significant mediator.

Now, let's add a mediator. This is still in the world of path analysis.

The evidence for that is that, that direct effect did not go completely away it was still significant after we added the mediator.

From the mediator to y, That's known as the indirect effect on our outcome variable y.

So, what this image depicts is here in the middle uh, a person who is a mediator.

Is we can predict the mediator from X. But to include this entire, sort of path diagram.

Now if we add an in a mediator variable, we will actually wind up with three regression equations here.

Of, our outcome and it significantly predicts the mediator. Now let's look at the full model, that's down here.

And if we can find a mediator variable that accounts for the same variance in y, then we're one step closer to understanding the mechanism, that links x and y.

So now, we have y and we have two sources of variance, two sources of systematic variance for a y We have x, the direct effect, but we also have the mediator.

So the question is, what happens to the predictive validity or the, the predictive value of X after you add in the mediator.

I am just going to show you two. So we have, our first one is uh, we can predict why from the mediator variable.

So what's a, what's a possible mediator or mechanism of that. Now, I, I just made this up and I just made up the data.

And secondly we can predict the mediate, mediator variable from X. So the first regression equation, what I'm doing is just picking off this piece.

Both predictors into one regression equation, so here's the full model, so to speak, as it has both the mediator and the, uh original predictor.

If, if X and Y are correlated because of the mediator, as I've said here on the slide. Then. X should no longer be significant.

So, x, y, m our predictor outcome, our mediator they will all be represented by rectangles because we observe them, they are scores on a survey.

So, that predictor variable, instead of just being something like extraversion, I could manipulate something and then, my outcome variable could be a true dependent variable, and we could look for a mediator of that experimental effect.

God is He that created the heavens and the earth, and what between them is, in six days, then seated Himself upon the Throne. Apart from Him, you have no protector neither mediator will you not remember?

Allah it is Who created the heavens and the earth, and that which is between them, in six Days. Then He mounted the Throne. Ye have not, beside Him, a protecting friend or mediator. Will ye not then remember?

Dotti For, for, for

For you. For me. For everybody.

For... For what?

...for... for... whatever.

Thanks for, uh, for fighting for us.

On the one hand, our need for security, for predictability, for safety, for dependability, for reliability, for permanence.

For Turkey, For Europe

'For you, for me'

For journey, for travel.

For me! For Aksinya!

For better, for worse.

For... for the murder?

For Gondor! For Gondor!

For hours... For days...